Similar Anatomical Structures Research Articles

Facing Differences of Similarity: Intra- and Inter-Correlation Unsupervised Learning for Chest X-Ray Anomaly Detection.

Anomaly detection can significantly aid doctors in interpreting chest X-rays. The commonly used strategy involves utilizing the pre-trained network to extract features from normal data to establish feature representations. However, when a pre-trained network is applied to more detailed X-rays, differences of similarity can limit the robustness of these feature representations. Therefore, we propose an intra- and inter-correlation learning framework for chest X-ray anomaly detection. Firstly, to better leverage the similar anatomical structure information in chest X-rays, we introduce the Anatomical-Feature Pyramid Fusion Module for feature fusion. This module aims to obtain fusion features with both local details and global contextual information. These fusion features are initialized by a trainable feature mapper and stored in a feature bank to serve as centers for learning. Furthermore, to Facing Differences of Similarity (FDS) introduced by the pre-trained network, we propose an intra- and inter-correlation learning strategy: (1) We use intra-correlation learning to establish intra-correlation between mapped features of individual images and semantic centers, thereby initially discovering lesions; (2) We employ inter-correlation learning to establish inter-correlation between mapped features of different images, further mitigating the differences of similarity introduced by the pre-trained network, and achieving effective detection results even in diverse chest disease environments. Finally, a comparison with 18 state-of-the-art methods on three datasets demonstrates the superiority and effectiveness of the proposed method across various scenarios.

Estimating the severity of obstructive sleep apnea during wakefulness using speech: A review

Obstructive sleep apnea (OSA) is a chronic breathing disorder during sleep that affects 10-30% of adults in North America. The gold standard for diagnosing OSA is polysomnography (PSG). However, PSG has several drawbacks, for example, it is a cumbersome and expensive procedure, which can be quite inconvenient for patients. Additionally, patients often have to endure long waitlists before they can undergo PSG. As a result, other alternatives for screening OSA have gained attention. Speech, as an accessible modality, is generated by variations in the pharyngeal airway, vocal tract, and soft tissues in the pharynx, which shares similar anatomical structures that contribute to OSA. Consequently, in this study, we aim to provide a comprehensive review of the existing research on the use of speech for estimating the severity of OSA. In this regard, a total of 851 papers were initially identified from the PubMed database using a specified set of keywords defined by population, intervention, comparison and outcome (PICO) criteria, along with a concatenated graph of the 5 most cited papers in the field extracted from ConnectedPapers platform. Following a rigorous filtering process that considered the preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach, 32 papers were ultimately included in this review. Among these, 28 papers primarily focused on developing methodology, while the remaining 4 papers delved into the clinical perspective of the association between OSA and speech. In the next step, we investigate the physiological similarities between OSA and speech. Subsequently, we highlight the features extracted from speech, the employed feature selection techniques, and the details of the developed models to predict OSA severity. By thoroughly discussing the current findings and limitations of studies in the field, we provide valuable insights into the gaps that need to be addressed in future research directions.

A novel model incorporating quantitative contrast-enhanced ultrasound into PI-RADSv2-based nomogram detecting clinically significant prostate cancer

The diagnostic accuracy of clinically significant prostate cancer (csPCa) of Prostate Imaging Reporting and Data System version 2 (PI-RADSv2) is limited by subjectivity in result interpretation and the false positive results from certain similar anatomic structures. We aimed to establish a new model combining quantitative contrast-enhanced ultrasound, PI-RADSv2, clinical parameters to optimize the PI-RADSv2-based model. The analysis was conducted based on a data set of 151 patients from 2019 to 2022, multiple regression analysis showed that prostate specific antigen density, age, PI-RADSv2, quantitative parameters (rush time, wash-out area under the curve) were independent predictors. Based on these predictors, we established a new predictive model, the AUCs of the model were 0.910 and 0.879 in training and validation cohort, which were higher than those of PI-RADSv2-based model (0.865 and 0.821 in training and validation cohort). Net Reclassification Index analysis indicated that the new predictive model improved the classification of patients. Decision curve analysis showed that in most risk probabilities, the new predictive model improved the clinical utility of PI-RADSv2-based model. Generally, this new predictive model showed that quantitative parameters from contrast enhanced ultrasound could help to improve the diagnostic performance of PI-RADSv2 based model in detecting csPCa.

Fetal cardiac ultrasound standard section detection model based on multitask learning and mixed attention mechanism

Fetal cardiac ultrasound is a valuable tool for screening fetal cardiac health during pregnancy. Ultrasound standard section testing is an essential part of fetal heart ultrasound diagnosis. Due to the many scattered and partially similar anatomical structures in standard ultrasound views of the fetal heart, the results strongly depend on the clinical experience and knowledge of the sonographer. To improve detection efficiency and reduce misdiagnosis and omission, we propose a single-stage fetal cardiac ultrasound standard plane detection model (FCUM) based on multitask learning and a hybrid attention mechanism to assist sonographers in diagnosis. The feature fusion pyramids of the backbone and detection networks of this model are each embedded with a hybrid attention mechanism module of our design, which enables the multitasking network to extract shared features more accurately and efficiently and improves the accuracy of the detection and classification networks. We designed a classification module for multilayer residual network feature fusion that leads to better classification and faster convergence time. We conducted comprehensive experiments on a dataset of fetal cardiac ultrasound images acquired with several types of devices and in different geographic regions. The experimental results show that our model outperforms baseline models such as YOLOv8 and ResNet-50 in terms of detection precision and classification accuracy.

Leaf developmental patterns of two species of Baccharis (Asteraceae): an analysis based on cell wall pectins

The cell wall plays a primary role in plant growth, which is regulated by cellulose microfibrils, proteins, and pectins. Regarding pectins, the rate of homogalacturonan (HG) methylesterification and the type of rhamnogalacturonan side chains can mediate leaf development. Herein, we evaluated pectic compounds of the cell walls by immunocytochemistry during the leaf development of Baccharis dracunculifolia DC. and Baccharis platypoda DC. Leaves in different maturation stages (from leaf bud to 9th node) were collected in a rupestrian field and fixed in formalin, acetic acid for anatomical and immunocytochemical analyses using monoclonal antibodies (JIM5, JIM7, LM5, and LM6). Both species showed similar anatomical structures based on peculiar cell wall pectic matrices. Baccharis platypoda leaves reach precocious maturation in the 3rd node, while B. dracunculifolia reaches maturation in the 6th node. These results seem to be related to HGs already de-methylesterified in the leaf primordia located in the leaf bud of B. platypoda. Epitopes of (1→5) α-l-arabinan occurred only in the collenchyma of B. platypoda, supporting cell wall stability. (1→4) β-D-galactans showed different fluorescence intensities in the two species, which changed during leaf development. The different rates of leaf tissue maturation between the two Baccharis species are influenced by variations in the pectic compounds of the cell walls, especially regarding the rate of HG methylesterification.

The Effects of Lithium Chloride Exposure on the Reproduction of Caenorhabditis elegans.

Caenorhabditis elegans ( C. elegans) are model organisms that share similar anatomical structures to humans. By exploring the effects of lithium chloride (LiCl) on C. elegans, we can collect crucial data regarding the compound's impact on patients taking psychiatric medications containing LiCl. Here we performed an egg retention assay on nematode populations to explore how LiCl can influence reproduction. We found a statistically significant difference in eggs retained between control and experimental groups, suggesting that LiCl has negative effects on reproductive health.

Numerical investigation of unruptured middle cerebral artery bifurcation aneurysms: influence of aspect ratio

An aneurysm is a disease condition, which is due to the pathological weakening of an arterial wall. These aneurysms are often found in various branch points and bifurcations of an artery in the cerebral circulation. Most aneurysms come to medical attention, either due to brain hemorrhages caused by rupture or found unruptured. To consider surgically invasive treatment modalities, clinicians need scientific methods such as, hemodynamic analysis to assess rupture risk. The arterial wall loses its structural integrity when wall shear stress (WSS) and other hemodynamic parameters exceed a certain threshold. In the present study, numerical simulations are carried out for unruptured middle cerebral artery (MCA) aneurysms. Three distinct representative sizes are chosen from a larger patient pool of 26 MCA aneurysms. Logically, these aneurysms represent three growth stages of any patient with similar anatomical structure. Simulations are performed to compare the three growth phases (with different aspect ratios) of an aneurysm and correlate their hemodynamic parameters. Simulations with patient specific boundary conditions reveal that, aneurysms with a higher aspect ratio (AR) correspond to an attendant decrease in both time-averaged wall shear stress (TAWSS) and spatial wall shear stress gradients (WSSG). Smaller MCAs were observed to have higher positive wall shear stress divergence (WSSD), exemplifying the tensile nature of arterial wall stretching. Present study identifies positive wall shear stress divergence (PWSSD) to be a potential biomarker for evaluating the growth of an aneurysm.

CONCEPTUAL ANALYSIS OF AKSHI-TARPANA AND ITS ROLE IN MYOPIA: A REVIEW

Myopia is a refractive error and a major eye health problem with significant social, personal, educational, and economic impacts. Globally, myopia affects 7.9 to 19.7% of the population. It is irreversible in progression and can be corrected with glasses, contact lenses, or refractive surgery. All these treatments are not patient-friendly and are also not the actual solution to the pathology occurring in the eye. The symptoms of myopia closely resemble the disease Timira involving the prathama and dwitiya patala of netra (the eye's anatomical structures). Furthermore, both diseases share similar anatomical structures and pathogenesis. There is a great need to find an ocular procedure to prevent and treat myopia having no or least adverse effects. In Ayurveda, kriyakalpa includes different topical therapeutic procedures for treating eye diseases. Tarpana is one among seven kriyakalpa that not only nourishes the eyeball but cures the diseases of the eye. Preventative and curative effects can be achieved with Tarpana karma for eye diseases. Using a special arrangement, an oily substance (ghrita) is kept in the eye for a predetermined period in Tarpana karma. This article reviews Tarpana karma and its role in myopia prevention and progression.

FetusMapV2: Enhanced fetal pose estimation in 3D ultrasound

Fetal pose estimation in 3D ultrasound (US) involves identifying a set of associated fetal anatomical landmarks. Its primary objective is to provide comprehensive information about the fetus through landmark connections, thus benefiting various critical applications, such as biometric measurements, plane localization, and fetal movement monitoring. However, accurately estimating the 3D fetal pose in US volume has several challenges, including poor image quality, limited GPU memory for tackling high dimensional data, symmetrical or ambiguous anatomical structures, and considerable variations in fetal poses. In this study, we propose a novel 3D fetal pose estimation framework (called FetusMapV2) to overcome the above challenges. Our contribution is three-fold. First, we propose a heuristic scheme that explores the complementary network structure-unconstrained and activation-unreserved GPU memory management approaches, which can enlarge the input image resolution for better results under limited GPU memory. Second, we design a novel Pair Loss to mitigate confusion caused by symmetrical and similar anatomical structures. It separates the hidden classification task from the landmark localization task and thus progressively eases model learning. Last, we propose a shape priors-based self-supervised learning by selecting the relatively stable landmarks to refine the pose online. Extensive experiments and diverse applications on a large-scale fetal US dataset including 1000 volumes with 22 landmarks per volume demonstrate that our method outperforms other strong competitors.

An anatomy-aware framework for automatic segmentation of parotid tumor from multimodal MRI

Magnetic Resonance Imaging (MRI) plays an important role in diagnosing the parotid tumor, where accurate segmentation of tumors is highly desired for determining appropriate treatment plans and avoiding unnecessary surgery. However, the task remains nontrivial and challenging due to ambiguous boundaries and various sizes of the tumor, as well as the presence of a large number of anatomical structures around the parotid gland that are similar to the tumor. To overcome these problems, we propose a novel anatomy-aware framework for automatic segmentation of parotid tumors from multimodal MRI. First, a Transformer-based multimodal fusion network PT-Net is proposed in this paper. The encoder of PT-Net extracts and fuses contextual information from three modalities of MRI from coarse to fine, to obtain cross-modality and multi-scale tumor information. The decoder stacks the feature maps of different modalities and calibrates the multimodal information using the channel attention mechanism. Second, considering that the segmentation model is prone to be disturbed by similar anatomical structures and make wrong predictions, we design anatomy-aware loss. By calculating the distance between the activation regions of the prediction segmentation and the ground truth, our loss function forces the model to distinguish similar anatomical structures with the tumor and make correct predictions. Extensive experiments with MRI scans of the parotid tumor showed that our PT-Net achieved higher segmentation accuracy than existing networks. The anatomy-aware loss outperformed state-of-the-art loss functions for parotid tumor segmentation. Our framework can potentially improve the quality of preoperative diagnosis and surgery planning of parotid tumors.

Eye Movement and Coma Recovery after Cardiac Arrest (P1-7.006)

<h3>Objective:</h3> To characterize eye movements in comatose patients after cardiac arrest (CA). <h3>Background:</h3> Most patients resuscitated from CA present with altered levels of consciousness due to brain injury. One potential marker of arousal recovery is evidence of eye movements. Brainstem pathways and thalamocortical projections responsible for eye movements share similar anatomic structures responsible for arousal. Our aim is to quantify eye movements using electro-oculography (EOG) data from patients presenting with CA. <h3>Design/Methods:</h3> We are conducting a retrospective study of patients presenting within 24 hours of CA. We identify eye movements on EOG over a period of up to 72 hours, excluding periods of clinical care, sedation, and epileptiform activity. We will study the association of eye movement frequency and the following outcomes by two weeks: 1. Present corneal or pupillary reflex; 2. Eye opening (to stimuli or spontaneously); 3. Command following. Patients are excluded if they have ophthalmoplegia, dementia, or traumatic brain injury. We will compare eye movement frequency in our comatose cohort to a separate cohort of non-comatose patients who underwent sleep studies. <h3>Results:</h3> Our preliminary data consists of 100 patients, mean (± SD) age of 60.2 (± 15.3) years and 38% were female. By 2 weeks, 85% had a pupillary reflex, 75% had a corneal reflex, 43% opened eyes to stimuli, and 21% followed commands. Of 151.5 hours of EOG time across four patients, we preliminarily found eye movements present in 0.56% of total time, with a median of 0.73% and range of 0% to 2.29%. This is comparable to our cohort of 47 patients who underwent sleep studies, with eye movements present in 3.2% of N2, 2.9% of N3, and 19.8% of REM. <h3>Conclusions:</h3> Eye movements in comatose patients may be indicative of recovery potential. Understanding these differences could have prognostic significance and help in triaging care and family discussions. <b>Disclosure:</b> The institution of Mr. Cronin has received research support from American Heart Association. Miss Hwang has nothing to disclose. The institution of an immediate family member of Miss Wack has received research support from Canon Medical Systems USA, Inc. Brian Pugsley has nothing to disclose. Mary Russo has nothing to disclose. Dr. Greer has received personal compensation in the range of $10,000-$49,999 for serving as an Editor, Associate Editor, or Editorial Advisory Board Member for Thieme, Inc. Dr. Greer has received personal compensation in the range of $5,000-$9,999 for serving as an Expert Witness for multiple. The institution of Dr. Greer has received research support from Becton Dickenson, Inc. Dr. Greer has received publishing royalties from a publication relating to health care. Dr. Ong has nothing to disclose.

Contrastive Semi-Supervised Learning for Domain Adaptive Segmentation Across Similar Anatomical Structures.

Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for medical image segmentation, yet need plenty of manual annotations for training. Semi-Supervised Learning (SSL) methods are promising to reduce the requirement of annotations, but their performance is still limited when the dataset size and the number of annotated images are small. Leveraging existing annotated datasets with similar anatomical structures to assist training has a potential for improving the model's performance. However, it is further challenged by the cross-anatomy domain shift due to the image modalities and even different organs in the target domain. To solve this problem, we propose Contrastive Semi-supervised learning for Cross Anatomy Domain Adaptation (CS-CADA) that adapts a model to segment similar structures in a target domain, which requires only limited annotations in the target domain by leveraging a set of existing annotated images of similar structures in a source domain. We use Domain-Specific Batch Normalization (DSBN) to individually normalize feature maps for the two anatomical domains, and propose a cross-domain contrastive learning strategy to encourage extracting domain invariant features. They are integrated into a Self-Ensembling Mean-Teacher (SE-MT) framework to exploit unlabeled target domain images with a prediction consistency constraint. Extensive experiments show that our CS-CADA is able to solve the challenging cross-anatomy domain shift problem, achieving accurate segmentation of coronary arteries in X-ray images with the help of retinal vessel images and cardiac MR images with the help of fundus images, respectively, given only a small number of annotations in the target domain. Our code is available at https://github.com/HiLab-git/DAG4MIA.

Sorption behaviour of three African tropical woods (Sapelli, Sipo, Kosipo) with similar anatomical structures from Cameroon

ABSTRACT This study aims to determine the sorption isotherms of three tropical woods from Cameroon to understand the conditions for their use and drying. The equilibrium moisture contents were experimentally determined using saturated salt solutions. Some anatomical parameters were also measured to assess the influence of the structure of these woods on their sorption behaviour. Among the sorption models tested, the Guggenheim-Anderson-de Boer model is the one that best fits the experimental points. Our results also reveal a strong correlation between EMCs and the size and number of anatomical parameters. Indeed, up to 40°C, Sipo which has the lowest wood density and larger vessel sizes and rays, contains more water at equilibrium that the two other woods. When temperatures increase above 50°C, Sapelli becomes more hygroscopic due to the expansion of its lumen cells. These findings highlight the need for specific data on the sorption behaviour of Central African woods, to efficiently manage their drying and use.

Dendro-anatomical Study for Identification of Pine at Korea

This study identified Korean red pine (Pinus densiflora) and exotic pines (Pinus resinosa, Pinus sylvestris) with a similar anatomical structure using a dendro-anatomical method that applied dendrochronology, tracheid length, and uniseriate ray cell size. Korean red pine samples were collected from 13 national parks, while exotic pine samples were secured from two wood importers. Tracheid length was measured by distinguishing earlywood from latewood, and uniseriate ray height and cell number were determined. As the exotic pine tree-ring chronology was consistent with the foreign standard tree-ring chronology and displayed high statistical significance, the country and region where the pine samples had been felled and the exact felling date were confirmed. According to the results, which compared tracheid length and uniseriate ray size, no difference was observed between the Korean red and Russian pines. However, the tracheid length of the Russian pines turned out to be slightly longer than the length of the Korean red pine. Additional research securing a larger number of exotic pines (P.resinosa, P.sylvestris) is required to yield more accurate results in the future.

Effects of Dietary Protein Level on the Microbial Composition and Metabolomic Profile in Postweaning Piglets

Since the human and porcine digestive systems have similar anatomical structures and physiological functions, pigs are a useful animal model for studying human digestive diseases. By investigating intestinal metabolites in piglets after weaning, this study attempted to identify the inherent connection between dietary protein levels and changes in the intestinal microbiota of piglets. Casein was employed as the only source of protein for the piglets in this study to avoid the influence of other protein sources. 14 weaning at 28-day-old piglets (6.9 ± 0.19 kg) formed into two dietary groups: 17% casein fed group (LP) and 30% casein fed group (HP). Piglets were allowed to free food and water during the 2-week experiment. Throughout the trial, the piglets' diarrhea index (1: no diarrhea and 3: watery diarrhea) and food intake were noted during the experiment. We discovered piglets fed a high-protein diet developed diarrhea throughout the duration of the research, whereas piglets fed a normal protein diet did not. In addition, the HP group had lower feed intake and body weight than the control group (P < 0.05). The HP diet influenced the content of short-chain and branched-chain fatty acids in the colon, including acetate and isovaleric acid. The ileal microbiota's 16S rRNA gene was sequenced, and it was discovered that the relative abundance of gastrointestinal bacteria differed between the HP and control groups. Dietary protein levels influenced bile acid biosynthesis, alpha-linolenic acid metabolism, phospholipid biosynthesis, arachidonic acid metabolism, fatty acid biosynthesis, retinol metabolism, arginine and proline metabolism, pyrimidine metabolism, tryptophan metabolism, and glycine and serine metabolism, according to gas chromatography-mass spectrometry analysis. Furthermore, a correlation analysis of the pooled information revealed a possible link between intestinal metabolites and specific bacteria species. These findings demonstrate that weaned piglets' microbiota composition and metabolites are modified by a high-protein diet and thus inducing severe postweaning diarrhea and inhibiting growth performance. However, the potential molecular mechanism of this regulation in the growth of piglets remains unclear.

FEATURES OF COGNITIVE DISORDERS IN PATIENTS WITH CHRONIC KIDNEY DISEASE AND THE EFFECT OF CYSTATIN C ON IT DEPENDING ON THE STAGE OF THE DISEASE

Chronic kidney disease (CKD) is a global problem of modern medicine. The neurological complications arising from this disease are an integral part of this condition because the kidneys and the brain have a similar anatomical structure of the vessels and a similar physiological structure of the processes occurring in them. Cognitive disorders (CD) that occur in CKD accompany almost every patient, regardless of the stage of the disease, and affect the psychological and social quality of life of patients. According to the literature, there are a number of substances called biomarkers, by determining which we can identify emerging neurological complications in the early stages of CKD. In our work, we used a new biomarker of renal function, cystatin C, which is characterized by free glomerular filtration and is not subject to tubular secretion. Its level in the blood serum of patients does not depend on the age, gender, muscle mass, ethnicity, diet, physical activity and bad habits of the patient.

Comparison of sheep scapula models created with polylactic acid and thermoplastic polyurethane filaments by three-dimensional modelling.

Three-dimensional (3D) printing technology is a rapid prototyping method that has recently been increasingly used in anatomy education. Magnetic resonance imaging, computed tomography, and 3D scanners are generally used to create 3D models. The aim of this study, which was performed without using the aforementioned devices, is to design sheep scapula models in a computer environment and compare bone models created with different filaments printed by a 3D printer with real bone. Photographs of sheep scapula were taken for modelling, and measurements were made from certain points. After the photographs were transferred to a computer environment, they were transformed into 3D using the Cinema 4D software, a computer-aided design program. Models were created using a 3D printer employing polylactic acid (PLA) and thermoplastic polyurethane (TPU) filaments. By comparing the models created with PLA and TPU filaments to the real bone, it was found that they have a similar anatomical structure, with dimensional-morphometric differences found at some points. It was also observed that the scapula model created with PLA filaments was more resistant to impacts than the real bone and that the model created with TPU filaments was more flexible, with very low fragility as compared to PLA and real bone. Therefore, this method allows obtaining a large number of durable models as an alternative to the real bone without the need for much manpower or equipment and without the need for a 3D reconstruction device.

Optical Flow with Learning Feature for Deformable Medical Image Registration

Deformable medical image registration plays a vital role in medical image applications, such as placing different temporal images at the same time point or different modality images into the same coordinate system. Various strategies have been developed to satisfy the increasing needs of deformable medical image registration. One popular registration method is estimating the displacement field by computing the optical flow between two images. The motion field (flow field) is computed based on either gray-value or handcrafted descriptors such as the scale-invariant feature transform (SIFT). These methods assume that illumination is constant between images. However, medical images may not always satisfy this assumption. In this study, we propose a metric learning-based motion estimation method called Siamese Flow for deformable medical image registration. We train metric learners using a Siamese network, which produces an image patch descriptor that guarantees a smaller feature distance in two similar anatomical structures and a larger feature distance in two dissimilar anatomical structures. In the proposed registration framework, the flow field is computed based on such features and is close to the real deformation field due to the excellent feature representation ability of the Siamese network. Experimental results demonstrate that the proposed method outperforms the Demons, SIFT Flow, Elastix, and VoxelMorph networks regarding registration accuracy and robustness, particularly with large deformations.

Multi-modal brain MRI images enhancement based on framelet and local weights super-resolution.

Magnetic resonance (MR) image enhancement technology can reconstruct high-resolution image from a low-resolution image, which is of great significance for clinical application and scientific research. T1 weighting and T2 weighting are the two common magnetic resonance imaging modes, each of which has its own advantages, but the imaging time of T2 is much longer than that of T1. Related studies have shown that they have very similar anatomical structures in brain images, which can be utilized to enhance the resolution of low-resolution T2 images by using the edge information of high-resolution T1 images that can be rapidly imaged, so as to shorten the imaging time needed for T2 images. In order to overcome the inflexibility of traditional methods using fixed weights for interpolation and the inaccuracy of using gradient threshold to determine edge regions, we propose a new model based on previous studies on multi-contrast MR image enhancement. Our model uses framelet decomposition to finely separate the edge structure of the T2 brain image, and uses the local regression weights calculated from T1 image to construct a global interpolation matrix, so that our model can not only guide the edge reconstruction more accurately where the weights are shared, but also carry out collaborative global optimization for the remaining pixels and their interpolated weights. Experimental results on a set of simulated MR data and two sets of real MR images show that the enhanced images obtained by the proposed method are superior to the compared methods in terms of visual sharpness or qualitative indicators.

Preservation of Features of Anatomical Polymorphism of Deschampsia antarctica É. Desv. (Poaceae) During In Vitro Clonal Reproduction

Abstract We studied the anatomical features of the leaf structure of Deschampsia antarctica É. Desv. (Poaceae) obtained from seeds collected from different habitats of the maritime Antarctic. These plants have been maintained in culture in vitro for more than 7 years. The plants include stable chromosome forms comprising diploids (2 n = 26), a diploid plant with B-chromosomes (2 n = 26 + 0–3B), a triploid with rearranged chromosome morphology, and myxoploids with different ratios of diploid and nondiploid cells in the root meristem. The D. antarctica plants that were studied generally had a similar anatomical structure in culture in vitro and in nature. At the same time, plants of different cultivated genotypes also displayed their own leaf structure features. In particular, qualitative features included asymmetric four-ribs and five-rib leaves instead of typical three-rib leaves for some genotypes, some individuals characterized by the presence of unicellular nonglandular pointed trichomes on an adaxial side, differences in vascular bundle sheath, and other features. No clear dependence of the anatomical structural features on the chromosomal status of the studied genotypes was evident. At the same time, differences were evident in traits that included the number of stomata, thickness of the outer cell walls of the epidermis, bundles sheath state, number of leaf ribs, and the persistent presence of trichomes under prolonged in vitro cloning. The findings indicate that D. antarctica plants collected from different locations of the maritime Antarctic for long-term cultivation in vitro under standard cultivation conditions retain the anatomical characteristics of the wild plants. The polymorphism of in vitro-cultivated plants is not related to the polymorphism of their karyotype, but is apparently due to the heterogeneity of the original plants and possibly the epigenetic fixation of a number of anatomical features produced in the natural microhabitats of D. antarctica populations.